A decision support methodology to enhance the competitiveness of the Turkish automotive industry

• Problem structuring for the competitiveness of the automotive industry.
• Analysis of competitiveness of the automotive industry using Bayesian Causal Networks.
• A decision support methodology to policy makers of the automotive industry.
• Sensitivity analysis using Bayesian Causal Networks.
• Improving the competitiveness of automotive industry based on national competitiveness.

Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.

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